MRI‐alone radiation therapy planning for prostate cancer: Automatic fiducial marker detection. Issue 5 (6th April 2016)

• Record Type: Journal Article
• Authors: Ghose, Soumya; Mitra, Jhimli; Rivest‐Hénault, David; Fazlollahi, Amir; Stanwell, Peter; Pichler, Peter; Sun, Jidi; Fripp, Jurgen; Greer, Peter B.; Dowling, Jason A.
• Is Part Of: Medical physics. Volume 43:Issue 5(2016)
• Page Start: 2218
• Page End: 2228
• Publication Date: 2016-04-06
• Subjects: biomedical MRI -- cancer -- Gaussian processes -- image registration -- Markov processes -- medical image processing -- radiation therapy; Clinical applications -- Markov processes -- Cancer -- Therapeutic applications, including brachytherapy -- Treatment planning -- Registration; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging -- Radiation therapy -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; prostate cancer -- radiation therapy -- spectral clustering -- template matching -- geometric registration; Gold -- Cluster analysis -- Computed tomography -- Cluster spectra -- Cancer -- Radiation therapy -- Manifolds -- Medical magnetic resonance imaging; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4944871
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: The feasibility of radiation therapy treatment planning using substitute computed tomography (sCT) generated from magnetic resonance images (MRIs) has been demonstrated by a number of research groups. One challenge with an MRI‐alone workflow is the accurate identification of intraprostatic gold fiducial markers, which are frequently used for prostate localization prior to each dose delivery fraction. This paper investigates a template‐matching approach for the detection of these seeds in MRI. Methods: Two different gradient echo T1 and T2* weighted MRI sequences were acquired from fifteen prostate cancer patients and evaluated for seed detection. For training, seed templates from manual contours were selected in a spectral clustering manifold learning framework. This aids in clustering "similar" gold fiducial markers together. The marker with the minimum distance to a cluster centroid was selected as the representative template of that cluster during training. During testing, Gaussian mixture modeling followed by a Markovian model was used in automatic detection of the probable candidates. The probable candidates were rigidly registered to the templates identified from spectral clustering, and a similarity metric is computed for ranking and detection. Results: A fiducial detection accuracy of 95% was obtained compared to manual observations. Expert radiation therapist observers were able to correctly identify all three implanted seeds on 11 of the 15 scans (the proposed method correctly identified all seeds on 10 of the 15). Conclusions: An novel automatic framework for gold fiducial marker detection in MRI is proposed and evaluated with detection accuracies comparable to manual detection. When radiation therapists are unable to determine the seed location in MRI, they refer back to the planning CT (only available in the existing clinical framework); similarly, an automatic quality control is built into the automatic software to ensure that all gold seeds are either correctly detected or a warning is raised for further manual intervention.